| AP | anteroposterior |
| APA | anticipatory postural adjustments |
| AUC | area under the receiver operating characteristic curve |
| ROC | receiver operating characteristic |
| BFs | biceps femoris short head |
| CNN | convolutional neural network |
| GRAD-CAM | gradient-weighted class activation mapping |
| HC | healthy control |
| iEMG | integrated surface electromyography |
| MaxIdx | maximum attention index |
| CenterIdx | center of mass index |
| HalfEnergyIdx | half-energy cumulative index |
| Ch | channel |
| MG | medial gastrocnemius |
| PD | Parkinson’s disease |
| RF | rectus femoris |
| SHAP | SHapley Additive exPlanations |
| SMOTE | synthetic minority oversampling technique |
| sEMG | surface electromyography |
| STW | sit-to-walk |
| TA | tibialis anterior |
| XAI | explainable artificial intelligence |
| UPDRS | Unified Parkinson’s Disease Rating Scale |
| H&Y | Hoehn and Yahr |
| ML | machine learning |
| DL | deep learning |
| RMS | root mean square |
| SD | standard deviation |
| LR | logistic regression |
| SVM | support vector machine with a linear kernel |
| XGBoost | extreme gradient boosting |
| CV | cross-validation |